The proliferation of digital information has allowed vast amounts of data to be generated very easily from almost anywhere in the world. Communication networks, such as the Internet, allow users from different locations to access this data. Because of the vastness of the amount of information, users typically do not know how to directly access data that they desire. To overcome this problem, data search engines were developed to allow users to search for relevant data. These search engines eventually transformed into remote servers that allowed user access outside of an immediate location. Thus, the vast amounts of data became easily accessible to users in any location by simply entering a search query into a remote data search engine. Results of the query were then returned to the user in a variety of server-based formats.
The effectiveness of a search query is dependent on several factors—adequacy of the search string, accessibility of relevant data by the search engine, and relevancy ranking of the data by the search engine. Timeliness of the search result can also play a major factor in search effectiveness as well. A poorly worded search string will not return favorable results to a user, and, even if properly worded, if the search engine does not have access to relevant data, the search results will be less than effective as well. If access is available, but the search engine ranks the search result relevancy poorly, the user will also be dissatisfied with the search results because they will be forced to review all of the returned results to find relevant data.
Users highly desire a search engine that can return relevant data quickly and efficiently. However, search engines are generally not flexible in allowing a user's own relevancy ranking to be included in the results. Search servers tend to operate in a somewhat isolated environment relative to their users. This typically requires that the search servers utilize some type of algorithm to attempt to predict the relevancy results of a given query. These types of search servers have met with various levels of success, but most are usually inflexible and unable to adapt when their algorithmic dependencies change. This requires that the algorithms themselves must be changed or adjusted to increase relevance and user satisfaction. Being able to account for variations in users' opinions of relevancy and providing users with relevant results without requiring constant manual algorithmic adjustments would prove to be extremely beneficial, both to the user and to the query search server.